{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "code_folding": [],
        "customInput": null,
        "hidden_ranges": [],
        "originalKey": "11b753f3-27c5-4cb5-a259-a7a40912c7f3",
        "showInput": false
      },
      "source": [
        "# Risk averse Bayesian optimization with input perturbations\n",
        "\n",
        "This notebook considers risk averse Bayesian optimization of objectives $f(x + \\Delta_x)$, where $x$ denotes the design variable and $\\Delta_x$ denotes the perturbations to the inputs that are applied at the implementation phase, such as manufacturing errors. \n",
        "The design variable $x$ is fully controlled by the practitioner, however, the input perturbation $\\Delta_x$ is only controllable at the experimentation phase and is determined by the environment once the decision $x$ is implemented, according to some probability distribution.\n",
        "This means that while optimizing the design, we can simulate $f(x)$ for any given $x$, however, once the optimization is done, the actual implemented solution becomes $x + \\Delta_x$.\n",
        "\n",
        "In this setting, we want to find high-performing designs that are also robust to the effects of the input perturbations. \n",
        "To do so, we will follow the Bayesian optimization of risk measures framework introduced in [1]. \n",
        "Please refer to the [Risk averse Bayesian optimization with environmental variables](https://botorch.org/docs/tutorials/risk_averse_bo_with_environmental_variables) notebook for additional background on this.\n",
        "\n",
        "In this notebook, we will use the `qNoisyExpectedImprovement` acquisition function to optimize the VaR risk measure at risk level $\\alpha=0.8$, computed w.r.t. the perturbations in the inputs. To do so, we will:\n",
        " - Use `InputPerturbation` input transform to add a set of samples of $\\Delta_x$ to each given $x$;\n",
        " - Calculate the joint posterior over these samples;\n",
        " - Use the `RiskMeasureMCObjective` to convert these joint samples into samples of the risk measure;\n",
        " - And use these risk measure samples to define the improvement in `qNoisyExpectedImprovement`.\n",
        "\n",
        "We will use the (negated) SixHumpCamel test function, and assume that the input perturbations follow a Gaussian distribution with standard deviation of 5% of the parameter space (truncated to the parameter bounds). \n",
        "During optimization, we will use 16 (qMC) samples of $\\Delta_x$ to approximate the VaR risk measure.\n",
        "\n",
        "VaR, the Value-at-Risk, is a risk measure that measures the worst possible outcome (small rewards or large losses) after excluding the worst outcomes with a total probability of $1 - \\alpha$. \n",
        "It is commonly used in finance for risk management, and corresponds to the $1 - \\alpha$ quantile of the random variable.\n",
        "\n",
        "Note: Risk measures are typically studied in the context of a minimization problem (including in [1]), since it makes more sense to minimize \"risk\", and treat the larger values as being undesirable. Since the default behavior in BoTorch is to maximize the objective, the `RiskMeasureMCObjective` (and its subclasses) is defined w.r.t. the lower tail of the random variable, i.e., by treating the smaller values as undesirable. With this implementation, all that is needed to minimize a risk measure (of the original objective) is to negate the objective, as is done in this notebook.   \n",
        "\n",
        "[1] [S. Cakmak, R. Astudillo, P. Frazier, and E. Zhou. Bayesian Optimization of Risk Measures. Advances in Neural Information Processing Systems 33, 2020.](https://arxiv.org/abs/2007.05554)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Install dependencies if we are running in colab\n",
        "import sys\n",
        "if 'google.colab' in sys.modules:\n",
        "    %pip install botorch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "code_folding": [],
        "collapsed": false,
        "executionStartTime": 1648579041696,
        "executionStopTime": 1648579043375,
        "hidden_ranges": [],
        "originalKey": "ebf568b8-adcc-43be-a799-541163a1804f",
        "requestMsgId": "d691ea18-fc13-4dbf-b076-22de5df56f59"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import warnings\n",
        "from time import time\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import torch\n",
        "from botorch import fit_gpytorch_mll\n",
        "from botorch.acquisition import qNoisyExpectedImprovement, qSimpleRegret\n",
        "from botorch.acquisition.risk_measures import VaR\n",
        "from botorch.models import SingleTaskGP\n",
        "from botorch.models.transforms import Standardize\n",
        "from botorch.models.transforms.input import InputPerturbation\n",
        "from botorch.sampling import SobolQMCNormalSampler\n",
        "from botorch.optim import optimize_acqf\n",
        "from botorch.utils.sampling import draw_sobol_samples, draw_sobol_normal_samples\n",
        "from botorch.utils.transforms import unnormalize\n",
        "from botorch.test_functions import SixHumpCamel\n",
        "from gpytorch import ExactMarginalLogLikelihood\n",
        "from torch import Tensor\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "SMOKE_TEST = os.environ.get(\"SMOKE_TEST\")\n",
        "BATCH_SIZE = 2 if not SMOKE_TEST else 1\n",
        "NUM_RESTARTS = 10 if not SMOKE_TEST else 2\n",
        "RAW_SAMPLES = 128 if not SMOKE_TEST else 4\n",
        "N_W = 16 if not SMOKE_TEST else 2\n",
        "NUM_ITERATIONS = 25 if not SMOKE_TEST else 2\n",
        "STD_DEV = 0.05\n",
        "ALPHA = 0.8\n",
        "\n",
        "tkwargs = {\"device\": \"cpu\", \"dtype\": torch.double}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "code_folding": [],
        "customInput": null,
        "hidden_ranges": [],
        "originalKey": "e7e62792-3280-40e9-ad9e-0bfb3c36e079",
        "showInput": true
      },
      "source": [
        "## Problem setup\n",
        "We will initialize the `SixHumpCamel` test function and define a wrapper around it to normalize the domain to $[0, 1]^2$."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "code_folding": [],
        "collapsed": false,
        "customInput": null,
        "executionStartTime": 1648579043418,
        "executionStopTime": 1648579043545,
        "hidden_ranges": [],
        "originalKey": "e1103a91-8192-4ecc-a1a8-e79dd538750b",
        "requestMsgId": "a0989add-2c98-48ea-8995-2d4cff84de75",
        "showInput": true
      },
      "outputs": [],
      "source": [
        "test_function = SixHumpCamel(negate=True)\n",
        "dim = test_function.dim\n",
        "\n",
        "\n",
        "def evaluate_function(X: Tensor) -> Tensor:\n",
        "    return test_function(unnormalize(X, test_function.bounds)).view(*X.shape[:-1], 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "code_folding": [],
        "customInput": null,
        "hidden_ranges": [],
        "originalKey": "0f810e1e-ca4b-4376-bd8f-ecd1a27af515",
        "showInput": true
      },
      "source": [
        "### Model initialization\n",
        "We will initialize the `SingleTaskGP` model on $8$ Sobol points. \n",
        "In doing so, we will also pass in the `InputPerturbation`. We will re-initialize `InputPerturbation` with a new set `perturbation_set` at every model training to ensure adequate coverage of the perturbation space."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "code_folding": [],
        "collapsed": false,
        "customInput": null,
        "executionStartTime": 1648579043590,
        "executionStopTime": 1648579043842,
        "hidden_ranges": [],
        "originalKey": "81b5fa3f-fa50-4e57-93fa-7353c9560c86",
        "requestMsgId": "9707eaf5-0f67-4171-b871-c3ae291c2a4d",
        "showInput": true
      },
      "outputs": [],
      "source": [
        "bounds = torch.stack([torch.zeros(dim), torch.ones(dim)]).to(**tkwargs)\n",
        "train_X = draw_sobol_samples(bounds=bounds, n=8, q=1).squeeze(-2).to(**tkwargs)\n",
        "train_Y = evaluate_function(train_X)\n",
        "\n",
        "\n",
        "def train_model(train_X: Tensor, train_Y: Tensor) -> SingleTaskGP:\n",
        "    r\"\"\"Returns a `SingleTaskGP` model trained on the inputs\"\"\"\n",
        "    intf = InputPerturbation(\n",
        "        perturbation_set=draw_sobol_normal_samples(d=dim, n=N_W, **tkwargs) * STD_DEV,\n",
        "        bounds=bounds,\n",
        "    )\n",
        "    model = SingleTaskGP(\n",
        "        train_X, train_Y, input_transform=intf\n",
        "    )\n",
        "    mll = ExactMarginalLogLikelihood(model.likelihood, model)\n",
        "    fit_gpytorch_mll(mll)\n",
        "    return model\n",
        "\n",
        "\n",
        "model = train_model(train_X, train_Y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "code_folding": [],
        "customInput": null,
        "hidden_ranges": [],
        "originalKey": "a0c807ee-19f8-4c02-ae17-63f0d00a12b8",
        "showInput": true
      },
      "source": [
        "### Define a helper function that performs the BO step\n",
        "The helper function will initialize the `qNoisyExpectedImprovement` acquisition function with the risk measure objective, and optimize it to find the candidate to evaluate."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "code_folding": [],
        "collapsed": false,
        "customInput": null,
        "executionStartTime": 1648579043887,
        "executionStopTime": 1648579044027,
        "hidden_ranges": [],
        "originalKey": "465569ac-b49e-4f9c-9f62-4a205b456697",
        "requestMsgId": "df0c214b-e2a9-4fa8-b919-13e515800d9c",
        "showInput": true
      },
      "outputs": [],
      "source": [
        "risk_measure = VaR(alpha=ALPHA, n_w=N_W)\n",
        "\n",
        "\n",
        "def optimize_acqf_and_get_observation():\n",
        "    r\"\"\"Optimizes the acquisition function, and returns a new candidate and observation.\"\"\"\n",
        "    acqf = qNoisyExpectedImprovement(\n",
        "        model=model,\n",
        "        X_baseline=train_X,\n",
        "        sampler=SobolQMCNormalSampler(sample_shape=torch.Size([128])),\n",
        "        objective=risk_measure,\n",
        "        prune_baseline=True,\n",
        "    )\n",
        "\n",
        "    candidate, _ = optimize_acqf(\n",
        "        acq_function=acqf,\n",
        "        bounds=bounds,\n",
        "        q=BATCH_SIZE,\n",
        "        num_restarts=NUM_RESTARTS,\n",
        "        raw_samples=RAW_SAMPLES,\n",
        "    )\n",
        "\n",
        "    new_observations = evaluate_function(candidate)\n",
        "    return candidate, new_observations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "code_folding": [],
        "customInput": null,
        "hidden_ranges": [],
        "originalKey": "561849ea-4637-4128-9db0-599fe37c1234",
        "showInput": true
      },
      "source": [
        "## Perform the Bayesian optimization loop\n",
        "The BO loop iterates the following steps:\n",
        "- Given the surrogate model, maximize the acquisition function to find the candidate(s) to evaluate;\n",
        "- Observe $f(x)$ for each candidate;\n",
        "- Update the surrogate model with the new observation(s).\n",
        "\n",
        "Note: Running this may take a while."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "code_folding": [],
        "collapsed": false,
        "customInput": null,
        "executionStartTime": 1648579044081,
        "executionStopTime": 1648579222644,
        "hidden_ranges": [],
        "originalKey": "620f1dd9-d789-4448-b7f6-ce203bf8c61b",
        "requestMsgId": "09cd5df2-50d1-4d73-b1a6-18d98975aeaf",
        "showInput": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting iteration 0, total time: 0.000 seconds.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting iteration 1, total time: 2.604 seconds.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting iteration 2, total time: 6.995 seconds.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting iteration 3, total time: 9.517 seconds.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting iteration 4, total time: 12.045 seconds.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting iteration 5, total time: 14.884 seconds.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting iteration 6, total time: 17.721 seconds.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting iteration 7, total time: 21.850 seconds.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting iteration 8, total time: 33.036 seconds.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting iteration 9, total time: 40.206 seconds.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting iteration 10, total time: 46.402 seconds.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting iteration 11, total time: 63.250 seconds.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting iteration 12, total time: 71.446 seconds.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting iteration 13, total time: 79.062 seconds.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting iteration 14, total time: 87.870 seconds.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting iteration 15, total time: 99.159 seconds.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting iteration 16, total time: 106.404 seconds.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting iteration 17, total time: 115.135 seconds.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting iteration 18, total time: 124.253 seconds.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting iteration 19, total time: 137.365 seconds.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting iteration 20, total time: 144.159 seconds.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting iteration 21, total time: 147.352 seconds.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting iteration 22, total time: 152.641 seconds.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting iteration 23, total time: 161.821 seconds.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting iteration 24, total time: 165.349 seconds.\n"
          ]
        }
      ],
      "source": [
        "start_time = time()\n",
        "\n",
        "for i in range(NUM_ITERATIONS):\n",
        "    print(f\"Starting iteration {i}, total time: {time() - start_time:.3f} seconds.\")\n",
        "    # optimize the acquisition function and get the observations\n",
        "    candidate, observations = optimize_acqf_and_get_observation()\n",
        "\n",
        "    # update the model with new observations\n",
        "    train_X = torch.cat([train_X, candidate], dim=0)\n",
        "    train_Y = torch.cat([train_Y, observations], dim=0)\n",
        "    model = train_model(train_X, train_Y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "code_folding": [],
        "customInput": null,
        "hidden_ranges": [],
        "originalKey": "3bcbd671-d272-4a89-8b25-f2af1220cfe1",
        "showInput": true
      },
      "source": [
        "### Find the solution to implement\n",
        "We will choose the solution to implement as the previously evaluated point that maximizes the posterior expectation of the risk measure. Since this expectation is not available in closed form, we will use its qMC estimate as a surrogate. We will use a larger `perturbation_set` here to get a more precise estimate."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "code_folding": [],
        "collapsed": false,
        "customInput": null,
        "executionStartTime": 1648579222717,
        "executionStopTime": 1648579223162,
        "hidden_ranges": [],
        "originalKey": "71d05725-90a6-4c77-9881-09cede69e98f",
        "requestMsgId": "f2aac029-9a23-41ca-ae94-406ad8fbd9e2",
        "showInput": true
      },
      "outputs": [],
      "source": [
        "# update the input transform of the already trained model\n",
        "new_intf = InputPerturbation(\n",
        "    perturbation_set=draw_sobol_normal_samples(d=dim, n=128, **tkwargs) * STD_DEV,\n",
        "    bounds=bounds,\n",
        ").eval()\n",
        "model.input_transform = new_intf\n",
        "\n",
        "risk_measure = VaR(alpha=ALPHA, n_w=128)\n",
        "expected_risk_measure = qSimpleRegret(model=model, objective=risk_measure)\n",
        "\n",
        "with torch.no_grad():\n",
        "    expected_rm_values = expected_risk_measure(train_X.unsqueeze(-2))\n",
        "expected_final_rm, max_idx = expected_rm_values.max(dim=0)\n",
        "final_candidate = train_X[max_idx]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "code_folding": [],
        "customInput": null,
        "hidden_ranges": [],
        "originalKey": "9b547b03-1693-4aac-8259-3ec21af0f091",
        "showInput": true
      },
      "source": [
        "### Plotting the risk measure corresponding to the best observed point over iterations\n",
        "As before, we define the best observed point as the previously evaluated point that maximizes the posterior expectation of the risk measure."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "code_folding": [],
        "collapsed": false,
        "customInput": null,
        "executionStartTime": 1648579223341,
        "executionStopTime": 1648579223966,
        "hidden_ranges": [],
        "originalKey": "8e81e7f5-2f0d-4afa-a083-41a7f9707a2d",
        "requestMsgId": "7855cba5-5393-4a3d-bb16-e90cb5cdb4df",
        "showInput": true
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "bento_obj_id": "140106045051520",
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "best_observed = torch.zeros(NUM_ITERATIONS + 1, **tkwargs)\n",
        "for i in range(NUM_ITERATIONS + 1):\n",
        "    best_observed[i] = expected_rm_values[: 6 + i * BATCH_SIZE].max()\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12, 8))\n",
        "ax.plot(best_observed)\n",
        "ax.set_xlabel(\"iterations\")\n",
        "ax.set_ylabel(\"risk measure\")\n",
        "ax.set_title(\"Best Observed Risk Measure\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "code_folding": [],
        "customInput": null,
        "hidden_ranges": [],
        "originalKey": "f5662c6c-d4be-4e6c-8dda-3d00bfe0eb90",
        "showInput": true
      },
      "source": [
        "### Plotting the true risk measure to see how we did\n",
        "We can use the input transform and the risk measure we previously defined to make this part easier!\n",
        "\n",
        "We plot both the response surface, $f(x)$, and the risk measure surface, $\\rho[f(x + \\Delta_x)]$, and mark the best risk averse solution found on both plots. \n",
        "The plots are restricted to $[0.3, 0.7]^2$ to highlight more promising areas of the solution space."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "code_folding": [],
        "collapsed": false,
        "customInput": null,
        "executionStartTime": 1648579223976,
        "executionStopTime": 1648579225433,
        "hidden_ranges": [],
        "originalKey": "9b85d409-31c6-4de9-b108-c494bd4e4ba6",
        "requestMsgId": "cae96c86-8c72-448d-8007-7a7965c0bcfe",
        "showInput": true
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1728x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "bento_obj_id": "140105842553136",
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "n_plot = 100\n",
        "\n",
        "fig, axes = plt.subplots(ncols=2, figsize=(24, 10))\n",
        "\n",
        "for i, ax in enumerate(axes):\n",
        "    # generate a grid of `x` points to evaluate for plotting\n",
        "    x_ = np.linspace(0.3, 0.7, n_plot)\n",
        "    x1, x2 = np.meshgrid(x_, x_)\n",
        "    eval_x_grid = torch.cat(\n",
        "        [torch.from_numpy(x1).unsqueeze(-1), torch.from_numpy(x2).unsqueeze(-1)], dim=-1\n",
        "    )\n",
        "    if i == 0:\n",
        "        plot_values = evaluate_function(eval_x_grid).view(n_plot, n_plot)\n",
        "        ax.set_title(\"Function $f(x)$ and Solution Found\")\n",
        "    else:\n",
        "        # add `delta_x` to each point, evalute the objective, and calculate the risk measure\n",
        "        eval_x_dx = new_intf(eval_x_grid)\n",
        "        eval_y = evaluate_function(eval_x_dx)\n",
        "        plot_values = risk_measure(eval_y).view(n_plot, n_plot)\n",
        "        ax.set_title(\"Objective $\\\\rho[f(x + \\Delta_x)]$ and Solution Found\")\n",
        "    contours = ax.contourf(x1, x2, plot_values, levels=40)\n",
        "    plt.colorbar(contours, ax=ax)\n",
        "    ax.scatter(final_candidate[0], final_candidate[1], marker=\"*\", color=\"red\", s=500)\n",
        "    ax.set_xlabel(\"$x_1$\")\n",
        "    ax.set_ylabel(\"$x_2$\")\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "python3",
      "language": "python",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}
